Senior AI Engineer - Forward Deployed; FDE

Atlanta, United States | Posted on 08/07/2026

We're hiring a Senior AI Engineer who took an unusual path - and we want that path specifically. You've spent 8-12 years as a serious software engineer, much of it building cloud-native systems and platforms on AWS. And in the last year, you've gone deep into AI: building agents, RAG systems, and LLM-powered applications - not reading about them, building them.

We're not looking for ten years of GenAI experience. Nobody has that. We're looking for deep engineering fundamentals with recent, real AI depth on top - because the hardest problems in AI right now are engineering problems: reliability, cost, latency, security, and evaluation of non-deterministic systems. That's your home turf.

You'll work across both sides of our stack: shipping AI capabilities in the product, and building the platform primitives underneath them - model access, agent orchestration, retrieval, and evaluation. You'll own systems end to end, make the architecture calls in your area, and raise the bar for the engineers around you. Senior IC role - lead by building, not by managing.

This is also a forward-deployed role. We build with our enterprise customers, not just for them - so you'll spend real time embedded with client teams: inside their systems, their security constraints, and their conference rooms, making our AI work in their world. Then you'll bring what the field taught you back into the product, so the next deployment is faster than the last.

• Build AI capabilities.
• Design, build, and ship agents, Agentic workflows, and LLM-powered features - from prototype to production, end to end.
• Build retrieval that actually works: chunking and embedding strategies, vector search, hybrid search and re-ranking, and honest measurement of retrieval quality.
• Integrate models and tools cleanly:
  Anthropic, OpenAI, and Bedrock model APIs; tool and data access via the Model Context Protocol (MCP), with its security considerations handled, not hand-waved.
• Deploy with our enterprise customers.
• Embed with client teams: run technical discovery, map their workflows and systems, and shape the AI solution that fits their environment - then build and ship it yourself.
• Integrate with the systems enterprises actually run: identity and SSO (SAML/OIDC, Okta, Active Directory), enterprise APIs, data warehouses and legacy databases, and systems of record like Salesforce, Service Now, or SAP.
• Deploy into customer environments - their AWS accounts, private VPCs, restricted networks - and design for their security model, not just ours.
• Be the technical face of the company on the ground: run working sessions and demos with client engineers and business stakeholders, and navigate enterprise Info Sec and architecture reviews with credibility.
• Close the loop: turn field learnings - integration patterns, platform gaps, recurring requests - into product and platform improvements.

• Build our model-access layer: unified interface across providers, routing on cost/latency/capability, caching, rate limits, and cost tracking per feature.
• Build agent orchestration and lifecycle plumbing - state and memory, retries, guardrails, versioning, and rollback - on frameworks like Lang Graph or CrewAI where they help, custom where they don't.
• Stand up the evaluation harness: automated evals and regression tests wired into CI/CD so AI changes ship with evidence, not vibes.
• Instrument everything: tracing of agent reasoning and tool calls, cost and latency dashboards, quality monitoring.

• Own AWS infrastructure for AI workloads - EKS/ECS, Lambda, IAM, networking, infrastructure-as-code - built for security and scale from day one.
• Operate what you build: production ownership, on-call sanity, and performance/cost tuning as usage grows.

• Drive design reviews and set the technical patterns others follow - you make the architecture calls in your area and can defend the trade-offs.
• Mentor 1-2 engineers and level up the team's AI engineering practices.
• Make pragmatic build-vs-buy calls in a fast-moving ecosystem without chasing every shiny framework.

• 8-12 years of software engineering experience, with significant time building cloud-native systems, backend services, or platforms - platform engineering background strongly preferred.
• Deep, hands-on AWS expertise: EKS/ECS, Lambda and serverless patterns, IAM and networking, and infrastructure-as-code (Terraform or CDK).…